Hi,

I'm doing a project where I have the presence of a plant, and I would like to see if environmental conditions affect the presence of this plant. I have been doing a Binomial Logistic Regression model, and I have successfully been able to detect how individual conditions affect the plant (pH, turbidity and 12 heavy metal concentrations, one is shown as 'As' here). Is there any way I can analysis two or more independent variables in Rstudio?

This is the script I have been using up to now.

Many thanks,

Julie

names(dframe1)

summary(dframe1)

hist(dframe1$presence, col="dark red")

pairs(dframe1, panel = panel.smooth)

#OUTLIERS

dotchart(dframe1$presence,

xlab = "Values of the data",

ylab = "Order of the data")

#PLOT POTENTIAL RELATIONSHIPS

plot (dframe1$presence ~ dframe1$As)

abline (lm(dframe1$presence ~ dframe1$As), col = "red", lwd = 3)

###Build a model ####

model1 <- glm(presence ~ As,

family = binomial (link = logit),

data = dframe1)

AIC(model1)

###Model Validation ####

plot(model1)

devresid <- resid(model1, type = "deviance")

hist(devresid) # deviance residuals >2 may indicate a poorly fitting model

install.packages("arm")

library(arm)

predicted.values <- predict(model1)

residuals <- resid(model1, type = "deviance")

binnedplot(predicted.values, residuals)

###Model Selection ####

drop1(model1, test='Chi')

###Interpret the model####

summary (model1)

summary.lm (model1)

exp(coef(model1))

exp(confint(model1))

(model1$null.deviance - model1$deviance) / model1$null.deviance

###Visualise the model ####

plot (dframe1$presence ~ dframe1$As,

ylab = "Probability of S. latifolium presence",

xlab = "As Concentration of rhyne water (mg/L)" ,

las=1, col = "blue")

summary(dframe1)

# Step 1: Making a table of prediction data (pdat)

pdat <- expand.grid(ph = seq(54.9,82,1))

pdat

# Step 2: Making a file containing the predicted data (pred)

pred <- predict (model1, newdata = pdat, type= "response", se.fit = TRUE)

pred

# Step 3: combine the predictions with the predictors,

# into a final dataframe (predframe)

predframe <- data.frame (pdat, presence = pred$fit, se = pred$se.fit)

predframe

# Step 4: Add the fitted line

lines (predframe$presence ~ predframe$As, col="red", lwd = 2)

lines (predframe$presence+predframe$se ~ predframe$As, col="red", lty = 2)

lines (predframe$presence-predframe$se ~ predframe$As, col="red", lty = 2)